

from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase			:				List[Any]   =     logging.get_logger(__name__)

# General docstring
lowercase			:				Dict   =     "ResNetConfig"

# Base docstring
lowercase			:				str   =     "microsoft/resnet-50"
lowercase			:				Tuple   =     [1, 20_48, 7, 7]

# Image classification docstring
lowercase			:				Tuple   =     "microsoft/resnet-50"
lowercase			:				str   =     "tiger cat"

lowercase			:				Any   =     [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 3    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = "relu"						)		->       int:
														super().__init__()
														A      :					List[Any]      		=					nn.Convad(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						kernel_size=__UpperCAmelCase    ,						stride=__UpperCAmelCase    ,						padding=kernel_size // 2    ,						bias=__UpperCAmelCase						)
														A      :					List[str]      		=					nn.BatchNormad(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					ACTaFN[activation] if activation is not None else nn.Identity()






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Tensor:
														A      :					List[str]      		=					self.convolution(__UpperCAmelCase						)
														A      :					List[str]      		=					self.normalization(__UpperCAmelCase						)
														A      :					List[Any]      		=					self.activation(__UpperCAmelCase						)
														return hidden_state







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														A      :					Optional[int]      		=					ResNetConvLayer(
														    config.num_channels    ,						config.embedding_size    ,						kernel_size=7    ,						stride=2    ,						activation=config.hidden_act						)
														A      :					Any      		=					nn.MaxPoolad(kernel_size=3    ,						stride=2    ,						padding=1						)
														A      :					Optional[Any]      		=					config.num_channels






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Tensor:
														A      :					Dict      		=					pixel_values.shape[1]
														if num_channels != self.num_channels:
																					raise ValueError(
																					    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''						)
														A      :					Tuple      		=					self.embedder(__UpperCAmelCase						)
														A      :					Optional[int]      		=					self.pooler(__UpperCAmelCase						)
														return embedding







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 2						)		->       Optional[Any]:
														super().__init__()
														A      :					Optional[int]      		=					nn.Convad(__UpperCAmelCase    ,						__UpperCAmelCase    ,						kernel_size=1    ,						stride=__UpperCAmelCase    ,						bias=__UpperCAmelCase						)
														A      :					Dict      		=					nn.BatchNormad(__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Tensor:
														A      :					Optional[Any]      		=					self.convolution(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					self.normalization(__UpperCAmelCase						)
														return hidden_state







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = "relu"						)		->       Dict:
														super().__init__()
														A      :					Union[str, Any]      		=					in_channels != out_channels or stride != 1
														A      :					Tuple      		=					(
														    ResNetShortCut(__UpperCAmelCase    ,						__UpperCAmelCase    ,						stride=__UpperCAmelCase						) if should_apply_shortcut else nn.Identity()
														)
														A      :					Optional[Any]      		=					nn.Sequential(
														    ResNetConvLayer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						stride=__UpperCAmelCase						)    ,						ResNetConvLayer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						activation=__UpperCAmelCase						)    ,						)
														A      :					int      		=					ACTaFN[activation]






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Dict:
														A      :					Union[str, Any]      		=					hidden_state
														A      :					str      		=					self.layer(__UpperCAmelCase						)
														A      :					Optional[int]      		=					self.shortcut(__UpperCAmelCase						)
														hidden_state += residual
														A      :					int      		=					self.activation(__UpperCAmelCase						)
														return hidden_state







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = "relu"    ,						__UpperCAmelCase = 4						)		->       Tuple:
														super().__init__()
														A      :					Union[str, Any]      		=					in_channels != out_channels or stride != 1
														A      :					Union[str, Any]      		=					out_channels // reduction
														A      :					int      		=					(
														    ResNetShortCut(__UpperCAmelCase    ,						__UpperCAmelCase    ,						stride=__UpperCAmelCase						) if should_apply_shortcut else nn.Identity()
														)
														A      :					Any      		=					nn.Sequential(
														    ResNetConvLayer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						kernel_size=1						)    ,						ResNetConvLayer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						stride=__UpperCAmelCase						)    ,						ResNetConvLayer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						kernel_size=1    ,						activation=__UpperCAmelCase						)    ,						)
														A      :					Tuple      		=					ACTaFN[activation]






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														A      :					Union[str, Any]      		=					hidden_state
														A      :					Optional[Any]      		=					self.layer(__UpperCAmelCase						)
														A      :					List[str]      		=					self.shortcut(__UpperCAmelCase						)
														hidden_state += residual
														A      :					str      		=					self.activation(__UpperCAmelCase						)
														return hidden_state







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 2    ,						__UpperCAmelCase = 2    ,						)		->       int:
														super().__init__()

														A      :					Optional[int]      		=					ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

														A      :					int      		=					nn.Sequential(
														    # downsampling is done in the first layer with stride of 2
														    layer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						stride=__UpperCAmelCase    ,						activation=config.hidden_act						)    ,						*[layer(__UpperCAmelCase    ,						__UpperCAmelCase    ,						activation=config.hidden_act						) for _ in range(depth - 1						)]    ,						)






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Tensor:
														A      :					Union[str, Any]      		=					input
														for layer in self.layers:
																					A      :					Optional[Any]      		=					layer(__UpperCAmelCase						)
														return hidden_state







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														super().__init__()
														A      :					str      		=					nn.ModuleList([]						)
														# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
														self.stages.append(
														    ResNetStage(
														        __UpperCAmelCase    ,						config.embedding_size    ,						config.hidden_sizes[0]    ,						stride=2 if config.downsample_in_first_stage else 1    ,						depth=config.depths[0]    ,						)						)
														A      :					Any      		=					zip(config.hidden_sizes    ,						config.hidden_sizes[1:]						)
														for (in_channels, out_channels), depth in zip(__UpperCAmelCase    ,						config.depths[1:]						):
																					self.stages.append(ResNetStage(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						depth=__UpperCAmelCase						)						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = True						)		->       BaseModelOutputWithNoAttention:
														A      :					Optional[int]      		=					() if output_hidden_states else None

														for stage_module in self.stages:
																					if output_hidden_states:
																												A      :					str      		=					hidden_states + (hidden_state,)

																					A      :					Optional[Any]      		=					stage_module(__UpperCAmelCase						)

														if output_hidden_states:
																					A      :					Dict      		=					hidden_states + (hidden_state,)

														if not return_dict:
																					return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

														return BaseModelOutputWithNoAttention(
														    last_hidden_state=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	ResNetConfig
							UpperCAmelCase_						:		Tuple								=	'''resnet'''
							UpperCAmelCase_						:		Optional[Any]								=	'''pixel_values'''
							UpperCAmelCase_						:		Union[str, Any]								=	True




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[str]:
														if isinstance(__UpperCAmelCase    ,						nn.Convad						):
																					nn.init.kaiming_normal_(module.weight    ,						mode='''fan_out'''    ,						nonlinearity='''relu'''						)
														elif isinstance(__UpperCAmelCase    ,						(nn.BatchNormad, nn.GroupNorm)						):
																					nn.init.constant_(module.weight    ,						1						)
																					nn.init.constant_(module.bias    ,						0						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=False						)		->       int:
														if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[str]      		=					value



lowercase			:				Optional[Any]   =     r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowercase			:				int   =     r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"







@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''		,       _SCREAMING_SNAKE_CASE		,       )
class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														super().__init__(__UpperCAmelCase						)
														A      :					Optional[int]      		=					config
														A      :					Optional[int]      		=					ResNetEmbeddings(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					ResNetEncoder(__UpperCAmelCase						)
														A      :					List[Any]      		=					nn.AdaptiveAvgPoolad((1, 1)						)
														# Initialize weights and apply final processing
														self.post_init()






							@add_start_docstrings_to_model_forward(__UpperCAmelCase						)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC    ,						output_type=__UpperCAmelCase    ,						config_class=_CONFIG_FOR_DOC    ,						modality='''vision'''    ,						expected_output=_EXPECTED_OUTPUT_SHAPE    ,						)
							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None						)		->       BaseModelOutputWithPoolingAndNoAttention:
														A      :					List[str]      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					List[str]      		=					return_dict if return_dict is not None else self.config.use_return_dict

														A      :					List[str]      		=					self.embedder(__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					self.encoder(
														    __UpperCAmelCase    ,						output_hidden_states=__UpperCAmelCase    ,						return_dict=__UpperCAmelCase						)

														A      :					Any      		=					encoder_outputs[0]

														A      :					Union[str, Any]      		=					self.pooler(__UpperCAmelCase						)

														if not return_dict:
																					return (last_hidden_state, pooled_output) + encoder_outputs[1:]

														return BaseModelOutputWithPoolingAndNoAttention(
														    last_hidden_state=__UpperCAmelCase    ,						pooler_output=__UpperCAmelCase    ,						hidden_states=encoder_outputs.hidden_states    ,						)







@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,       _SCREAMING_SNAKE_CASE		,       )
class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       Any:
														super().__init__(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					config.num_labels
														A      :					str      		=					ResNetModel(__UpperCAmelCase						)
														# classification head
														A      :					int      		=					nn.Sequential(
														    nn.Flatten()    ,						nn.Linear(config.hidden_sizes[-1]    ,						config.num_labels						) if config.num_labels > 0 else nn.Identity()    ,						)
														# initialize weights and apply final processing
														self.post_init()






							@add_start_docstrings_to_model_forward(__UpperCAmelCase						)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,						output_type=__UpperCAmelCase    ,						config_class=_CONFIG_FOR_DOC    ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,						)
							def       snake_case			(					self    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						)		->       ImageClassifierOutputWithNoAttention:
														A      :					Union[str, Any]      		=					return_dict if return_dict is not None else self.config.use_return_dict

														A      :					List[str]      		=					self.resnet(__UpperCAmelCase    ,						output_hidden_states=__UpperCAmelCase    ,						return_dict=__UpperCAmelCase						)

														A      :					int      		=					outputs.pooler_output if return_dict else outputs[1]

														A      :					Tuple      		=					self.classifier(__UpperCAmelCase						)

														A      :					List[str]      		=					None

														if labels is not None:
																					if self.config.problem_type is None:
																												if self.num_labels == 1:
																																			A      :					Dict      		=					'''regression'''
																												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																			A      :					Tuple      		=					'''single_label_classification'''
																												else:
																																			A      :					int      		=					'''multi_label_classification'''
																					if self.config.problem_type == "regression":
																												A      :					str      		=					MSELoss()
																												if self.num_labels == 1:
																																			A      :					Optional[Any]      		=					loss_fct(logits.squeeze()    ,						labels.squeeze()						)
																												else:
																																			A      :					List[Any]      		=					loss_fct(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					elif self.config.problem_type == "single_label_classification":
																												A      :					Any      		=					CrossEntropyLoss()
																												A      :					Union[str, Any]      		=					loss_fct(logits.view(-1    ,						self.num_labels						)    ,						labels.view(-1						)						)
																					elif self.config.problem_type == "multi_label_classification":
																												A      :					Any      		=					BCEWithLogitsLoss()
																												A      :					List[Any]      		=					loss_fct(__UpperCAmelCase    ,						__UpperCAmelCase						)

														if not return_dict:
																					A      :					Tuple      		=					(logits,) + outputs[2:]
																					return (loss,) + output if loss is not None else output

														return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase    ,						logits=__UpperCAmelCase    ,						hidden_states=outputs.hidden_states						)







@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''		,       _SCREAMING_SNAKE_CASE		,       )
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														super().__init__(__UpperCAmelCase						)
														super()._init_backbone(__UpperCAmelCase						)

														A      :					Tuple      		=					[config.embedding_size] + config.hidden_sizes
														A      :					Optional[Any]      		=					ResNetEmbeddings(__UpperCAmelCase						)
														A      :					Dict      		=					ResNetEncoder(__UpperCAmelCase						)

														# initialize weights and apply final processing
														self.post_init()






							@add_start_docstrings_to_model_forward(__UpperCAmelCase						)
							@replace_return_docstrings(output_type=__UpperCAmelCase    ,						config_class=_CONFIG_FOR_DOC						)
							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None						)		->       BackboneOutput:
														A      :					List[Any]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					Any      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)

														A      :					Optional[Any]      		=					self.embedder(__UpperCAmelCase						)

														A      :					int      		=					self.encoder(__UpperCAmelCase    ,						output_hidden_states=__UpperCAmelCase    ,						return_dict=__UpperCAmelCase						)

														A      :					Dict      		=					outputs.hidden_states

														A      :					Dict      		=					()
														for idx, stage in enumerate(self.stage_names						):
																					if stage in self.out_features:
																												feature_maps += (hidden_states[idx],)

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												output += (outputs.hidden_states,)
																					return output

														return BackboneOutput(
														    feature_maps=__UpperCAmelCase    ,						hidden_states=outputs.hidden_states if output_hidden_states else None    ,						attentions=__UpperCAmelCase    ,						)









from math import factorial







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					real
														if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					str      		=					[1] * rank
														else:
																					A      :					Dict      		=					rank




							def __repr__(					self						)		->       List[str]:
														return (
														    f'{self.real}+'
														    f'{"+".join(str(__UpperCAmelCase						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
														)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					List[Any]      		=					self.duals.copy()
														while cur[-1] == 0:
																					cur.pop(-1						)
														return Dual(self.real    ,						__UpperCAmelCase						)




							def __add__(					self    ,						__UpperCAmelCase						)		->       Any:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					return Dual(self.real + other    ,						self.duals						)
														A      :					int      		=					self.duals.copy()
														A      :					Any      		=					other.duals.copy()
														if len(__UpperCAmelCase						) > len(__UpperCAmelCase						):
																					o_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														elif len(__UpperCAmelCase						) < len(__UpperCAmelCase						):
																					s_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														A      :					List[str]      		=					[]
														for i in range(len(__UpperCAmelCase						)						):
																					new_duals.append(s_dual[i] + o_dual[i]						)
														return Dual(self.real + other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__add__




							def __sub__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return self + other * -1




							def __mul__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Union[str, Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i * other						)
																					return Dual(self.real * other    ,						__UpperCAmelCase						)
														A      :					Optional[Any]      		=					[0] * (len(self.duals						) + len(other.duals						) + 1)
														for i, item in enumerate(self.duals						):
																					for j, jtem in enumerate(other.duals						):
																												new_duals[i + j + 1] += item * jtem
														for k in range(len(self.duals						)						):
																					new_duals[k] += self.duals[k] * other.real
														for index in range(len(other.duals						)						):
																					new_duals[index] += other.duals[index] * self.real
														return Dual(self.real * other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__mul__




							def __truediv__(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Optional[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i / other						)
																					return Dual(self.real / other    ,						__UpperCAmelCase						)
														raise ValueError




							def __floordiv__(					self    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i // other						)
																					return Dual(self.real // other    ,						__UpperCAmelCase						)
														raise ValueError






							def __pow__(					self    ,						__UpperCAmelCase						)		->       Any:
														if n < 0 or isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					raise ValueError('''power must be a positive integer'''						)
														if n == 0:
																					return 1
														if n == 1:
																					return self
														A      :					Dict      		=					self
														for _ in range(n - 1						):
																					x *= self
														return x






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							if not callable(lowerCamelCase_		):
														raise ValueError('''differentiate() requires a function as input for func'''		)
							if not isinstance(lowerCamelCase_     ,				(float, int)		):
														raise ValueError('''differentiate() requires a float as input for position'''		)
							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														raise ValueError('''differentiate() requires an int as input for order'''		)
							A      :					Any      		=					Dual(lowerCamelCase_     ,				1		)
							A      :					Dict      		=					func(lowerCamelCase_		)
							if order == 0:
														return result.real
							return result.duals[order - 1] * factorial(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






		def        snake_case__		(    lowerCamelCase_		):
									return y**2 * y**4

		print(differentiate(f, 9, 2))






